쉐도잉 연습: 5 open source tools that replaced my $320/mo AI stack... - 영상으로 영어 말하기 배우기

Recently, I did some math, and it made me realize how broken the world is.

A cursor, $20.

Claude Max, $100.

GPT Pro, another $100.

Gemini Ultra, another $100.

Not to mention 11 Labs, DeepSeq, and all the API keys I signed up for two years ago and forgot about.

My AI addiction got so expensive that I had to quit alcohol and Zin.

And I'm not the only one, because alcohol sales in the United States are currently sitting at historic lows.

And Big AI is the one to blame for this.

But that was my wake-up call, and since then, I've cancelled all these subscriptions, and now self-host the ultimate AI stack for developers that's not only cheaper, but far more productive.

In today's video, we'll look at a bunch of free and open-source projects you can run on your own server.

We'll make them work together, while still having the option to tap into the intelligence of big models like Claude and GPT Sol.

It is September 7th, 2026, and you're watching The Code Report.

The first tool we need to talk about is OLAMA, because every AI stack first needs a model.

For most people, this means sending your proprietary code to a trillion dollar corporation and trusting them to keep it totally safe, or you can use OLAMA and do everything locally.

I've talked about this tool many other times on the channel, and essentially it's like Docker for large language models.

It gives you a simple command line interface and API for downloading and running models on your own machine.

This is great if you want to try out the latest Chinese models or any other open weight models, and what's even better is that all your prompts stay private and the inference cost is zero,

which means the model continues to work even after your credit card is declined.

The big problem, though, is that you probably don't own the hardware to run anything close to state-of-the-art.

The small models will run almost anywhere, but running a frontier-sized model would require a small data center, and that's a big problem if you're a Vibe engineer who needs to write top-tier slot.

Luckily, there's a self-hostable solution to this called 9Router, which is an AI tool that sits between your AI tools and dozens of different model providers through one local endpoint.

Instead of juggling nine different API keys, 9Router provides one door with an OpenAI compatible proxy that allows you to point everything through localhost.

But the thing that's really cool about 9Router is fallback tiers.

On tier one, you might have an existing subscription like Claude Max that you already pay for.

Then on tier two, you might have a cheap pay-per-token OpenAI model as a backup.

Then tier three has all of free providers like Chinese models, vertex trial credits, and so on.

That means if you max out Claude Max, it'll automatically roll over to the next tier without you having to do anything.

But in addition to that, 9Router also tracks usage and will compress your tool output to reduce token consumption, which will ultimately save you a bunch of money.

But if you're still eating billions of tokens per day, then you need to know about Headroom, which is a context compression layer for AI agents.

You ask AI to center a div, and to do that, it reads 50,000 lines of your package lock JSON file, then it evaporates an Olympic-sized swimming pool worth of water,

just to realize it needs to install Tailwind CSS.

Headroom sits between your application and the model provider,

and compresses everything from tool outputs to log files and any other useless chunks before it actually goes to the model, which would be billable input tokens.

And one clever design feature of this tool is that it's reversible.

The compressed content that ships to the model is cached on your local machine, which means if the model ever needs it, it can always retrieve it.

This sounds awesome, but at this point, you might be wondering, where do I deploy all this stuff?

Well, the answer, of course, is Hostinger, the sponsor of today's video.

Not only is it one of the most affordable places to host your own virtual private server, but they also have an entire Docker catalog built into the panel,

which means every single open source project I talk about in this video can be hosted there with a single click.

In fact, you could have all these tools running and working together on the same VPS.

Head over to hostinger.com slash fireship and use my coupon code to save a bunch of money spinning up your own VPS.

Once you have that done, the next tool you'll want to install there is Diffy.

Everything we've talked about so far is infrastructure, but now it's time to build an actual app that'll make us rich.

Instead of trying to engineer everything through prompts, Diffy is a visual builder that allows us to drag nodes on a canvas.

This has been a game changer for Horse Tinder and its AI matchmaking feature.

The app will send Diffy each horse's profile information, then a visual workflow retrieves compatible horses from the database, and uses a large language model to explain each match.

Like Thunder and Butternuts are a 94% match because they both enjoy trail rides and biting children.

Then the entire workflow is exposed as an API, so the front-end application can just call it whenever a horse swipes right.

Amazing, but building apps as a human became outdated in Q2 2026 after the release of open hands

which is an open source tool

that allows you to fire yourself this tool is a top performing autonomous coding agent on swebench verified

which is the benchmark where models fix real github issues instead of trying to build

and fix things just open up github issues

and let open hands do all the work essentially it gives

you a command center where you can self-host an army of ai agents

that are always on and working in the background and because Because it's running on your own virtual private server, you have the option to run it with OpenAI or Anthropic models,

or use the local LLMs that you installed earlier with OLAMA.

And now at this point, we have our own private AI stack that could build any type of software you can imagine.

But this is just the tip of the iceberg, and there's tons of other open source applications in Hostinger's Docker catalog that you can install with zero hassle.

Check out the link below

and use coupon code Fireship to get your own virtual private server on Hostinger up and running right now.

This has been the Code Report, thanks for watching, and I will see you in the next one.

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이 레슨의 어휘와 말하기 포인트

이 B2 수준 말하기 레슨은 영상 “5 open source tools that replaced my $320/mo AI stack..”을(를) 바탕으로 합니다. 가장 자주 반복되는 단어는 다음과 같습니다: model, tool, tier, code, private. 이 영상에는 섀도잉할 문장 63개와 단어 1161개가 있습니다. 말하는 구간의 길이는 5:22입니다. 화자는 분당 약 217단어로 빠르게 말하므로 연음과 약하게 발음되는 소리가 많습니다. 영어에서 가장 많이 쓰이는 3,000단어에 속하는 단어가 80%뿐이라 어휘가 어려운 편입니다.

이 영상의 핵심 어휘

영상에서 가장 어려운 단어 15개를 발음, 뜻과 함께 정리했습니다.

단어	발음	뜻
token 명사	/ˈtoʊkən/	토큰
stack 명사	/stæk/	무더기
install 동사	/ɪnˈstɔl/	설치하다, 깔다
hosting 명사	/ˈhoʊstɪŋ/	호스팅
workflow 명사	/ˈwɝkfloʊ/	워크플로
prompt 동사	/pɹɑmpt/	부추기다
coupon 명사	/ˈkuːˌpɑn/	쿠폰
catalogue 명사	/ˈkæt.əˌlɔɡ/	목록, 카탈로그
subscription 명사	/səbˈskɹɪpʃən/	구독
cursor 명사	/ˈkɜːsə/	커서
iceberg 명사	/ˈaɪsbɝɡ/	빙산
juggling 명사	/ˈd͡ʒʌɡəlɪŋ/	저글링
matchmaking 명사	/ˈmæt͡ʃˌmeɪkɪŋ/	중매
vertex 명사	/ˈvɝˌtɛks/	정점
cache 명사	/kæʃ/	캐시

영상에 나오는 구동사

단어	발음	뜻
wake up 동사	/ˌweɪk ˈʌp/	눈뜨다, 일어나다

이 영상의 문법

화자가 가장 많이 쓰는 문형을 영상 속 실제 표현과 함께 정리했습니다.

문형	영상 속 표현
현재완료 have/has + 과거분사 — 과거의 일이 지금도 관련이 있을 때	I've cancelled · I've talked · we've talked
수동태 be + 과거분사 — 누가 하는지보다 무슨 일이 일어나는지에 초점	is declined · is cached · can be hosted
관계절 who / which + 절 — 사람이나 사물에 대한 추가 정보	consumption, which will · Headroom, which is · machine, which means

주의할 발음

화자는 you're, I've, we'll 같은 축약형과 약화된 형태를 13번 사용합니다. 들리는 대로 짧게 발음하세요.

“sh”와 “zh” 소리: subscription /səbˈskɹɪpʃən/, benchmark /ˈbɛn(t)ʃmɑːk/, cache /kæʃ/, compression /kəmˈpɹɛʃ.ən/, addiction /əˈdɪkˌʃən/
긴 단어 — 강세 위치에 주의: compatible /kəmˈpæt.ə.bəl/, evaporate /ɪˈvæp.ə.ɹeɪt/, reversible /ɹəˈvɜː(ɹ)sɪbəl/, proprietary /pɹəˈpɹaɪ.ə.tə.ɹi/, autonomous /ɔˈtɑnəməs/

한국어 화자가 어려워하는 소리:

/f/ — ㅍ(/p/)으로 바꾸지 말고 윗니를 아랫입술에 대기: workflow /ˈwɝkfloʊ/, fallback /ˈfɔːl.bæk/, inference /ˈɪn.fə.ɹəns/, verify /ˈvɛɹɪfaɪ/, frontier /ˈfɹʌntɪə/
/v/ — ㅂ(/b/)과 구별하기: provider /pɹəˈvaɪ.də(ɹ)/, retrieve /ɹɪˈtɹiːv/, evaporate /ɪˈvæp.ə.ɹeɪt/, reversible /ɹəˈvɜː(ɹ)sɪbəl/, vertex /ˈvɝˌtɛks/
/r/과 /l/ 구별하기: reversible /ɹəˈvɜː(ɹ)sɪbəl/, builder /ˈbɪl.dəɹ/

이 영상으로 연습하는 방법

먼저 말하지 않고 영상을 끝까지 듣고 모르는 단어를 적어 둡니다.
0.75배속으로 한 문장씩 섀도잉을 시작하고, 익숙해지면 보통 속도로 돌아갑니다.
자신의 목소리를 녹음해 원본과 비교하고, token, stack, install 같은 단어에 특히 주의합니다.

쉐도잉이란? 영어 실력을 빠르게 키우는 과학적 방법

쉐도잉(Shadowing)은 원래 전문 통역사 훈련을 위해 개발된 언어 학습 기법으로, 다언어 학자인 Dr. Alexander Arguelles에 의해 대중화된 방법입니다. 핵심 원리는 간단하지만 매우 강력합니다: 원어민의 영어를 들으면서 1~2초의 짧은 지연으로 즉시 소리 내어 따라 말하는 것——마치 '그림자(shadow)'처럼 화자를 따라가는 것입니다. 문법 공부나 수동적인 청취와 달리, 쉐도잉은 뇌와 입 근육이 동시에 실시간으로 영어를 처리하고 재현하도록 훈련합니다. 연구에 따르면 이 방법은 발음 정확도, 억양, 리듬, 연음, 청취력, 말하기 유창성을 크게 향상시킵니다. IELTS 스피킹 준비와 자연스러운 영어 소통을 원하는 분들에게 특히 효과적입니다.

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이 레슨의 어휘와 말하기 포인트

이 영상의 핵심 어휘

영상에 나오는 구동사

이 영상의 문법

주의할 발음

이 영상으로 연습하는 방법

쉐도잉이란? 영어 실력을 빠르게 키우는 과학적 방법